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With the rapid development of computer science and technology, the Chinese petroleum industry has ushered in the era of big data. In this study, by collecting fracturing data from 303 horizontal wells in the Fuling Shale Gas Demonstration Area in China, a series of big data analysis studies was conducted using Pearson’s correlation coefficient, the unweighted pair group with arithmetic means method, and the graphical plate method to determine which is best. The fracturing parameters were determined through a series of big data analysis studies. The big data analysis process is divided into three main steps. The first is data preprocessing to screen out eligible, high-yielding wells. The second is a fracturing parameter correlation clustering analysis to determine the reasonableness of the parameters. The third is a big data panel method analysis of specific fracturing construction parameters to determine the optimal parameter range. The analyses revealed that the current amount of 100 mesh sand in the Fuling area is unreasonable; further, there are different preferred areas for different fracturing construction parameters. We have combined different fracturing parameter schemes by preferring areas. This analysis process is expected to provide new ideas regarding fracturing scheme design for engineers working on the frontline.

Random mobility of a node in wireless sensor networks (WSNs) causes the frequent changes in the network dynamics with increased cost in terms of energy and bandwidth. During data collections and transmission, they need the additional efforts to synchronize and schedule the activities of nodes. A key challenge is to maintain the global clock scale for synchronization of nodes at different levels to minimize the energy consumption and clock skew. It is also difficult to schedule the activities for effective utilization of slots allocated for aggregated data transmission. The paper proposes the Random Mobility and Heterogeneity-aware Hybrid Synchronization Algorithm (MHS) for WSN. The proposed algorithm uses the cluster-tree for efficient synchronization of CH and nodes in the cluster and network, level-by-level. The network consists of three nodes with random mobility and are heterogeneous regarding energy with static sink. All the nodes and CH are synchronized with the notion of the global timescale provided by the sink as a root node. With the random mobility of the node, the network structure frequently changes causing an increase in energy consumption. To mitigate this problem, MHS aggregate data with the notion of a global timescale throughout the network. Also, the hierarchical structure along with pair-wise synchronization reduces the clock skews hence energy consumption. In the second phase of MHS, the aggregated data packets are passed through the scheduled and synchronized slots using TDMA as basic MAC layer protocol to reduce the collision of packets. The results are extended by using the hybrid approach of scheduling and synchronization algorithm on the base protocol. The comparative results show that MHS is energy and bandwidth efficient, with increased throughput and reduced delay as compared with state-of-the-art solutions.

Se examina en este articulo la actual crisis economica global, desde una optica alternativa y distante de las visiones que predominan entre los analistas economicos convencionales, asi como de aquellas interpretaciones que postulan algunos pensadores “alternativos”. El autor –basandose en la que denomina “teoria economica comprensiva”- sostiene que en lo esencial, esta crisis hunde sus raices en una distorsion del sistema monetario imperante a nivel global, que esta significando que el dinero ha perdido su capacidad de cumplir sus funciones esenciales. Argumenta en seguida que las politicas keynesianas por las que se busca actualmente enfrentar esta crisis, lejos de contribuir a superarla la acentuaran y prolongaran, hasta que se establezca un nuevo sistema monetario, en el contexto de una diversa estructuracion del mercado, que no podra ocurrir hasta que se cumplan grandes reformas culturales, institucionales, juridicas y politicas.

Abstract An extended-reach [ERD] well was planned and successfully drilled in Block 16/26 of the U.K. Central North Sea with water-based drilling fluid in an area where wells have historically been drilled with mineral oil-based and ester-based drilling fluids. With increasingly stringent environmental regulations, the current utilization of mineral oil-based fluids is dependent upon shipping all cuttings back to shore for processing and subsequent disposal, resulting in high supplemental costs. Additionally, potential adverse weather conditions could temporarily preventing the transfer of cuttings from the platform to a supply vessel, thereby curtailing the drilling operations and further increasing drilling costs. The operator realized that significant savings to the current overall well costs could be realized if a water-based drilling fluid system were successfully used to drill the large-diameter hole interval that produced the highest volumes of cuttings.

peer-reviewed A key change in the move to Smart Grids (SGs) is the use of dynamic pricing; this together with less reliable energy from renewable resources makes optimising electricity use highly complex. For smart-devices to function in this envi- ronment, they must adapt to this complexity, while main- taining the flexibility to handle changing user behaviour pat- terns. Reinforcement Learning (RL) has been used to op- timise the scheduling of dynamic resources in SGs. It is proposed to provide smart-devices with knowledge of user intentions and actions by leveraging participatory sensing data. This, in consequence, will allow devices in the SG to tailor their operational schedule to users’ behaviour. With- out this data, the devices’ operation would be interrupted by user activity, leading to suboptimal results. Participa- tory sensing provides for both, the monitoring of parame- ters affecting devices operation (for example, temperature for a heating system) and access to detailed information about user behaviour and activity. The results obtained by our RL approach, clearly indicate that participatory sensing data indeed improve the performance of device scheduling when compared to static schemes resulting in a dramatic price reduction.

In Nature, the primary goal of any network is to survive. This is less obvious for engineering networks (electric power, gas, water, transportation systems etc.) that are expected to operate under normal conditions most of time. As a result, the ability of a network to withstand massive sudden damage caused by adverse events (or survivability) has not been among traditional goals in the network design. Reality, however, calls for the adjustment of design priorities. As modern networks develop toward increasing their size, complexity, and integration, the likelihood of adverse events increases too due to technological development, climate change, and activities in the political arena among other factors. Under such circumstances, a network failure has an unprecedented effect on lives and economy. To mitigate the impact of adverse events on the network operability, the survivability analysis must be conducted at the early stage of the network design. Such analysis requires the development of new analytical and computational tools. Computational analysis of the network survivability is the exponential time problem at least. The current paper describes a new algorithm, in which the reduction of the computational complexity is achieved by mapping an initial network topology with multiple sources and sinks onto a set of simpler smaller topologies with multiple sources and a single sink. Steps for further reducing the time and space expenses of computations are also discussed. 31 pages, 2 tables, 7 figures

The operational performance of the agricultural tractor can be increased by adjusting parameters such as tire inflation pressure, axle mass distribution and gauge opening, which is a very divergent factor in controlled-traffic production.The objective of the experiment was to measure the energy performance of a 93 kW agricultural tractor in intermediate grading operation in two slopes (flat and sloping) and three gauge configurations (closed, intermediate and open) in mobilized soil.Energy performance was determined from the following parameters: slip, engine speed, actual effective speed, hourly and specific fuel consumption; strength, power and performance on the drawbar.Turbo pressure and temperature monitoring was also performed at six different engine points.The experiment was conducted in double factorial arrangement (2 slopes and 3 gauges), with four replications, totaling 24 plots.Data were analyzed for normality and homoscedasticity of the residues, after ANOVA and when significant, the means test.Tilts and tire gauges did not differ statistically, so it can be concluded that depending on the operation do not interfere with operating performance. The operational performance of the agricultural tractor can be increased by adjusting parameters such as tire inflation pressure, axle mass distribution and gauge opening, which is a very divergent factor in controlled-traffic production.The objective of the experiment was to measure the energy performance of a 93 kW agricultural tractor in intermediate grading operation in two slopes (flat and sloping) and three gauge configurations (closed, intermediate and open) in mobilized soil.Energy performance was determined from the following parameters: slip, engine speed, actual effective speed, hourly and specific fuel consumption; strength, power and performance on the drawbar.Turbo pressure and temperature monitoring was also performed at six different engine points.The experiment was conducted in double factorial arrangement (2 slopes and 3 gauges), with four replications, totaling 24 plots.Data were analyzed for normality and homoscedasticity of the residues, after ANOVA and when significant, the means test.Tilts and tire gauges did not differ statistically, so it can be concluded that depending on the operation do not interfere with operating performance. The operational performance of the agricultural tractor can be increased by adjusting parameters such as tire inflation pressure, axle mass distribution and gauge opening, which is a very divergent factor in controlled-traffic production.The objective of the experiment was to measure the energy performance of a 93 kW agricultural tractor in intermediate grading operation in two slopes (flat and sloping) and three gauge configurations (closed, intermediate and open) in mobilized soil.Energy performance was determined from the following parameters: slip, engine speed, actual effective specific fuel consumption; strength, power and performance on the drawbar.Turbo pressure and temperature monitoring also performed at six different engine points.The experiment was conducted in double arrangement (2 slip, engine specific speed speed, hly and specific fuel consumption; hly power and performance on the drawbar.Turbo pressure and temperature monitoring was also performed at six different engine. يمكن زيادة الأداء التشغيلي للجرار الزراعي عن طريق ضبط المعلمات مثل ضغط نفخ الإطارات وتوزيع كتلة المحور وفتح المقياس، وهو عامل متباين للغاية في إنتاج حركة المرور الخاضعة للرقابة. كان الهدف من التجربة هو قياس أداء الطاقة لجرار زراعي بقوة 93 كيلو واط في عملية الدرجات المتوسطة في منحدرين (مسطحين ومنحدرين) وثلاثة تكوينات مقاييس (مغلقة ومتوسطة ومفتوحة) في التربة المعبأة. تم تحديد أداء الطاقة من المعلمات التالية: الانزلاق وسرعة المحرك والسرعة الفعلية الفعالة واستهلاك الوقود بالساعة والمحددة ؛ القوة والقوة والأداء على شريط السحب. كما تم إجراء مراقبة ضغط ودرجة حرارة التوربو في ست نقاط مختلفة للمحرك. تم إجراء التجربة بترتيب عاملي مزدوج (منحدرين و 3 مقاييس)، مع أربعة تكرارات، بلغ مجموعها 24 قطعة. تم تحليل البيانات من أجل الوضع الطبيعي والمتماثل للمخلفات، بعد ANOVA وعندما تكون كبيرة، يعني الاختبار. لم تختلف مقاييس الإطارات والإطارات من الناحية الإحصائية، لذلك يمكن استنتاج أنه لا يمكن أن تتداخل مع التشغيل. The operational performance of the agricultural tractor can be increased by adjusting parameters such as tire inflation pressure, axle mass distribution and gauge opening, which is a very divergent factor in controlled-traffic production.The objective of the experiment was to measure the energy performance of a 93 kW agricultural tractor in intermediate grading operation in two slopes (flat and sloping) and three gauge configurations (closed, intermediate and open) in mobilized soil.Energy performance was determined from the following parameters : slip, engine speed, actual effective speed, hourly and specific fuel consumption ; strength, power and performance on the drawbar.Turbo pressure and temperature monitoring was performed at six different engine points.The experiment was conducted in double factorial arrangement (2 slopes and 3 gau), with fourplications, total plots 24 plots.

Most methods for interpolating unstructured point clouds handle densely sampled point sets quite well but get into trouble when the point set contains regions with much sparser sampling, a situation often encountered in practice. In this paper, we present a new method that provides a better interpolation of sparsely sampled features. We pose the surface construction problem as finding the triangle mesh which minimizes the sum of all triangles' longest edge. Since searching for matching umbrellas among sparsely sampled points to yield a closed manifold shape is a difficult problem, we introduce suitable heuristics. Our algorithm first connects the points by triangles chosen in order of their longest edge and with the requirement that all edges must have at least two incident triangles. This yields a closed non-manifold shape which we call the Boundary Complex. Then we transform it into a manifold triangulation using topological operations. We show that in practice, runtime is linear to that of the Delaunay triangulation of the points. Source code is available online.